Package for using GPTwoSample
This module allows the user to compare two timelines with respect to diffferential expression.
It compares two timeseries against each other, depicting whether these two
timeseries were more likely drawn from the same function, or from
different ones. This prediction is defined by which covariance function pygp.covar you use.
Created on Jun 15, 2011
@author: Max Zwiessele, Oliver Stegle
-
class gptwosample.twosample.twosample.TwoSample(T, Y, covar_common=None, covar_individual_1=None, covar_individual_2=None)[source]
Bases: object
Run GPTwoSample on given data.
- Parameters:
- T : TimePoints [n x r x t] [Samples x Replicates x Timepoints]
- Y : ExpressionMatrix [n x r x t x d] [Samples x Replicates x Timepoints x Genes]
- Fields:
- T: Time Points [n x r x t] [Samples x Replicates x Timepoints]
- Y: Expression [n x r x t x d] [Samples x Replicates x Timepoints x Genes]
- X: Confounders [nrt x 1+q] [SamplesReplicatesTimepoints x T+q]
- lvm_covariance: GPLVM covaraince function used for confounder learning
- n: Samples
- r: Replicates
- t: Timepoints
- d: Genes
- q: Confounder Components
-
bayes_factors(likelihoods=None)[source]
get list of bayes_factors for all genes.
- returns:
- bayes_factor for each gene in Y
-
plot(xlabel='input', ylabel='ouput', title=None, interval_indices=None, alpha=None, legend=True, replicate_indices=None, shift=None, timeshift=False, *args, **kwargs)[source]
iterate through all genes and plot
-
predict_likelihoods(T, Y, message='Predicting Likelihoods: ', messages=False, priors=None, **kwargs)[source]
Predict all likelihoods for all genes, given in Y
parameters:
- indices : [int]
- list (or array-like) for gene indices to predict, if None all genes will be predicted
- message: str
- printing message
- kwargs: {...}
- kwargs for gptwosample.twosample.GPTwoSampleBase.predict_model_likelihoods()
-
predict_means_variances(interpolation_interval, indices=None, message='Predicting means and variances: ', *args, **kwargs)[source]
Predicts means and variances for all genes given in Y for given interpolation_interval
-
set_data(T, Y)[source]
Set data by time T and expression matrix Y:
Parameters:
- T : real [n x r x t]
- All Timepoints with shape [Samples x Replicates x Timepoints]
- Y : real [n x r x t x d]
- All expression values given in the form: [Samples x Replicates x Timepoints x Genes]
-
class gptwosample.confounder.confounder.TwoSampleConfounder(T, Y, q=4, lvm_covariance=None, init='random', covar_common=None, covar_individual_1=None, covar_individual_2=None)[source]
Bases: gptwosample.twosample.twosample.TwoSample
Run GPTwoSample on given Data
- Parameters:
- T : TimePoints [n x r x t] [Samples x Replicates x Timepoints]
- Y : ExpressionMatrix [n x r x t x d] [Samples x Replicates x Timepoints x Genes]
- q : Number of Confounders to use
- lvm_covariance : optional - set covariance to use in confounder learning
- init : [random, pca]
- Fields:
- T: Time Points [n x r x t] [Samples x Replicates x Timepoints]
- Y: Expression [n x r x t x d] [Samples x Replicates x Timepoints x Genes]
- X: Confounders [nrt x 1+q] [SamplesReplicatesTimepoints x T+q]
- lvm_covariance: GPLVM covaraince function used for confounder learning
- n: Samples
- r: Replicates
- t: Timepoints
- d: Genes
- q: Confounder Components
-
initialize_twosample_covariance(covar_common=<function <lambda> at 0x11189f410>, covar_individual_1=<function <lambda> at 0x11189f398>, covar_individual_2=<function <lambda> at 0x11189f320>)[source]
initialize twosample covariance with function covariance(XX), where XX
is a FixedCF with the learned confounder matrix.
default is SumCF([SqexpCFARD(1), FixedCF(self.K_conf.copy()), BiasCF()])
-
learn_confounder_matrix(ard_indices=None, x=None, messages=True, gradient_tolerance=1e-12, lvm_dimension_indices=None, gradcheck=False, maxiter=10000)[source]
Learn confounder matrix with this model.
Parameters:
- x : array-like
- If you provided an own lvm_covariance you have to specify
the X to use within GPLVM
- lvm_dimension_indices : [int]
- If you specified an own lvm_covariance you have to specify
the dimension indices for GPLVM
- ard_indices : [indices]
- If you provided an own lvm_covariance, give the ard indices of the covariance here,
to be able to use the correct hyperparameters for calculating the confounder covariance matrix.
-
class gptwosample.twosample.twosample_base.TwoSampleShare(covar, *args, **kwargs)[source]
Bases: gptwosample.twosample.twosample_base.TwoSampleBase
This class provides comparison of two Timeline Groups to each other.
see gptwosample.twosample.twosample_base.TwoSampleBase for detailed description of provided methods.
-
class gptwosample.twosample.twosample_base.TwoSampleSeparate(covar_individual_1, covar_individual_2, covar_common, **kwargs)[source]
Bases: gptwosample.twosample.twosample_base.TwoSampleBase
This class provides comparison of two Timeline Groups to one another, inlcuding timeshifts in replicates, respectively.
see gptwosample.twosample.twosample_base.TwoSampleBase for detailed description of provided methods.
Note that this model will need one covariance function for each model, respectively!
-
class gptwosample.twosample.twosample_base.TwoSampleBase(learn_hyperparameters=True, priors=None, initial_hyperparameters=None, **kwargs)[source]
Bases: object
TwoSampleBase object with the given covariance function covar.
-
bayes_factor(model_likelihoods=None)[source]
Return the Bayes Factor for the given log marginal likelihoods model_likelihoods
Parameters:
- model_likelihoods : {‘individual’: the individual likelihoods, ‘common’: the common likelihoods}
- The likelihoods calculated by
predict_model_likelihoods(training_data)
for given training data training_data.
-
get_data(model='common model fit', index=None)[source]
get inputs of model model with group index index.
If index is None, the whole model group will be returned.
-
get_learned_hyperparameters()[source]
Returns learned hyperparameters in model structure, if already learned.
-
get_model_likelihoods()[source]
Returns all calculated likelihoods in model structure. If not calculated returns None in model structure.
-
get_predicted_mean_variance()[source]
Get the predicted mean and variance as:
{'individual':{'mean':[pointwise mean], 'var':[pointwise variance]},
'common':{'mean':[pointwise mean], 'var':[pointwise variance]}}
If not yet predicted it will return ‘individual’ and ‘common’ empty.
-
plot(xlabel='input', ylabel='ouput', title=None, interval_indices=None, alpha=None, legend=True, replicate_indices=None, shift=None, *args, **kwargs)[source]
Plot the results given by last prediction.
Two Instance Plots of comparing two groups to each other:
Parameters:
- twosample_object : gptwosample.twosample
- GPTwoSample object, on which already ‘predict’ was called.
Differential Groups:
Non-Differential Groups:
- Returns:
- Proper rectangles for use in pylab.legend().
-
predict_mean_variance(interpolation_interval, hyperparams=None, interval_indices={'common model fit': None, 'individual model fit': None}, *args, **kwargs)[source]
Predicts the mean and variance of both models.
Returns:
{'individual':{'mean':[pointwise mean], 'var':[pointwise variance]},
'common':{'mean':[pointwise mean], 'var':[pointwise variance]}}
Parameters:
- interpolation_interval : [double]
- The interval of inputs, which shall be predicted
- hyperparams : {‘covar’:logtheta, ...}
- Default: learned hyperparameters. Hyperparams for the covariance function’s prediction.
- interval_indices : {‘common’:[boolean],’individual’:[boolean]}
- Indices in which to predict, for each group, respectively.
-
predict_model_likelihoods(training_data=None, interval_indices={'common model fit': None, 'individual model fit': None}, *args, **kwargs)[source]
Predict the probabilities of the models (individual and common) to describe the data.
It will optimize hyperparameters respectively.
Parameters:
- training_data : dict traning_data
- The training data to learn from. Input are time-values and
output are expression-values of e.g. a timeseries.
If not given, training data must be given previously by
gptwosample.twosample.basic.set_data.
- interval_indices: gptwosample.data.data_base.get_model_structure()
- interval indices, which assign data to individual or common model,
respectively.
- args : [..]
- see pygp.gpr.gp_base.GP
- kwargs : {..}
- see pygp.gpr.gp_base.GP
-
set_data(training_data)[source]
Set the data of prediction.
Parameters:
- training_data : dict traning_data
The training data to learn from. Input are time-values and
output are expression-values of e.g. a timeseries.
Training data training_data has following structure:
{'input' : {'group 1':[double] ... 'group n':[double]},
'output' : {'group 1':[double] ... 'group n':[double]}}


Pegaesys recognizes all single ASCII keys as commands: A-Z, 0-9, punctuation.
Control.LinkExecute "A"
snaps the cursor to a point.
Control.LinkExecute "X"
moves the cursor South.
If the single character is a short-cut key, it is executed and control is passed to the Pegaesys window.
Non ascii commands such as return and escape can be sent using DDE. It is necessary to specify the character in the form {###} where ### is the keyAscii numeric representation:
Control.LinkExecute "{13}" 'sends <enter>.
Control.LinkExecute "{27}" 'sends <esc>.


Venusaur
This is
pokemon
Grass
/
Poison
type of the old generation. At level 32, it
evolves
from
Ivysaur
. The final
evolution
of the
grass
starter
pokemon
Bulbasaur


As you can see below, Iceland is the leader of producing renewable energy with 100% renewable of it's whole energy production, followed by Ethiopia (99.95%) and Norway (98%). By far the most countries have a renewable production below 40%. There is no correlation between the amount of gas emissions and renewable energy production. China, United States, Russia and India are the largest global emitters of the world but they all score lower than 20% on producing renewable energy. Another prediction of % renewable energy per country could be the date they signed the Paris Agreement. From 22 april 2016 all countries had the change to sign The Paris Agreement. There is no correlation between renewable energy production and the signing date but most countries signed 6-8 months after they had the change.


Hi there, my name is Nitai Campbell and I am an experienced WordPress Developer based in Brisbane.
I build quality websites that are maintainable, responsive, and provide a good user experience.
Known for having a keen eye for detail, showing initiative, identifying and solving problems, and getting that job out the door - I welcome new and exciting opportunites.
Currently available for hire.
The Australian School of Meditation & Yoga (ASMY) needed a design refresh and site restructure to better showcase their individual centers and offerings.
Together with an Online Marketing expert we looked at existing website traffic trends, identified visitor pain points, and restructured the navigation and content flow to better meet visitors' needs and communicate ASMY's services effectively.
Using Figma I was able to quickly produce wireframes, mobile and desktop designs, work collarboratively on the design, and share design prototypes with the client.
One of the primary challenges was finding the balance between maintaining design integrity across the site and giving the client the ability to create their own layouts. ASMY has many centers around Australia which offer different types of programs. In order for them to be able to present the information attractively and clearly we used a combination of ACF and custom page builder modules that allow content managers to build modular page layouts.
Full Website redesign. Mobile and Desktop screens designed in Figma.
Built from the ground up in WordPress, using Bedrock, Sage 9, ACF.
Integrated Tribes Events plugin into theme with custom templates.
Custom layout modules created for Beaver Builder page builder.
Cloud Hosting Management for superior performance.
Samantha, Ayurvedic Lifestyle Coach and creator of Live to Serve Academy approached me for help with her website and online course delivery.
Working closely with her I helped with logo development, and designed the main facing website and LMS membership portal. This included detailed wireframes and designs for longform sales landing pages.
Custom themes were built using Sage 8 per the designs for both the front facing site and the LMS membership portal.
LearnDash was used for the online learning portal and customisations were included in the theme to create a seamless integration with the new design.
In order for Samantha to be able to manage all her customers effectively across different sales channels we really needed a WordPress membership system that could be controlled via Infusionsoft. For this reason we chose Memberium.
With the Infusionsoft integration that Memberium provides I was able to setup Infusionsoft sales funnels that would automatically handle the learning portal membership creation, user access, new member onboarding emails, follow up emails, and more.
Full website and LMS design.
Built from the ground up in WordPress, using Bedrock, Sage 8, ACF.
LMS built using WordPress, Memberium, and LearnDash.
Infusionsoft integration with learning portal to control LMS membership access.
Brandon Raynor the founder of Raynor College of Massage & Natural Therapies is a long term client of 5+ years who I manage multiple websites for including a WordPress multisite.
This particular project required a design refresh, content update, and migration from a multisite installation.
A new design was done to update the school's online brand and better present the course information as well as allow for online course enrolments. This included working with a copywriter to update the website content and improve readability and presentation.
A key component of this job was adding the ability to enrol and pay for upcoming courses with the unique requirement that each course would be sold in one particular currency and not be converted at checkout.
Additionally we wanted to be able to list upcoming courses chronologically. In order to achieve this I used WooCommerce to be able to create and sell the courses, and combined with ACF I was able to display those upcoming courses in relevant ares of the site, and create a filterable upcoming course schedule.
Additional features include: products being auto disabled after the course date passes, date programmable special prices such as Early Bird specials etc., setting course details such as start and end date, course teacher, and more.
As a later addon, a school graduate public directory was added which lists graduates of Raynor College of Massage & Natural Therapies. Using FacetWP and WordPress' custom post type meant I could create a browsable map which filtered graduates based on location, in a very short amount of time.
Full Website redesign and content update.
Migrated from multisite installation to clean WordPress build using Bedrock.
Theme built from the groupd up with Sage 8, ACF.
Course sales via WooCommerce. Required custom integration to allow selling different courses in different currencies without currency conversion.
Added the ability to display WooCommerce products as upcoming courses and filter by date, course type, etc.
Browsable Graduates directory/finder using Google Maps and location filtering with the help of FacetWP.
Managed copywriter to update website content.
Ability to work independantly or collarboratively with a team to meet deadlines and complete tasks.
Clear understanding and interrogation of design documents (PSD, wireframes).
Providing website and design solutions remotely and on-site for various clients.
Using the latest development workflows and tools, I have experience evaluating and planning projects, managing project scope, solving problems, and delivering on time and on budget.
Working autonomously for various long-term clients as a Freelance Web Developer with a focus on WordPress deployments, I am often required to see a project through from beginning to end and manage said projects indefinitely. As such I understand the importance of good development practices, good communication, and reliability.
Worked as part of a team of 7 in a boutique agency to produce varied high quality solutions using WordPress from start to finish.
Responsibilities included building pixel perfect mobile-first themes from the ground up. Updating and progressively enhancing existing WordPress sites. Troubleshooting bugs and solving technical issues. Training clients.
Worked remotely for various long-term and international clients as a Freelance Web Developer and Designer with a focus on WordPress deployments.
Apart from being a web developer, I enjoy spending time with my daughter, playing boardgames with friends, and cooking.
I believe life is a journey and am always trying to improve myself, one small step at a time (The Slight Edge).


Summary
The Postman is a feature rich REST client. Thousands of developers used it to browse, test and even documenting APIs. This post aims to provide basic information you need to start using Postman
Introduction
Started in 2012 with the idea of REST client Postman has improved itself to support all the feature you need for API development and testing. According to officials in 2014, it has 3+ million active users. You can make HTTP requests like GET, POST, PUT, DELETE from it. You can also perform automation test on your API using it. With monitoring feature, you can execute set of requests to check for its performance and response
It’s available as a Chrome extension and native app build on The Electron. I recommended using the native app because Chrome extension will be deprecated soon. You can download it from here. In this post, we will use service call Postman Echo to mock our API
Send your first request
Calling API from Postman is like a cakewalk. For simplicity, we will do a GET request to the server
- URL of API
- Request Type [GET, POST, PUT, DELETE]
- Send button To call the API
- Response Of API
- More information
- History of past API calls
Send Post data with Request:
While doing a POST request to the API you need to pass some data as a body. As given in the screenshot above, you can pass data while calling your API
Collections
Postman provides good way to Organize your related request. It’s called Collections. It’s like folder structure so you can browse API. Also You can use collections with Test script but this will be explained later. You can create and browse Collection as displayed below
Example
When you have different frontend and backend team, REST API design is the most important part. Frontend team doesn’t want to refactor their code because of misunderstanding about API design. Postman solves this problem with the concept called “Example”. You can save request/response parameter of your API as an Example. After saving your Example, You can share it with the team. You can also create a mock server with Postman mock server. frontend team can use it as an alternative to actual API
Conclusion
Postman is a day to day tool for any developer. Any backend developer can quickly check his API without any frontend application. Also, the developer can write test script which will help them to do impact analysis of their change on API. Frontend developer can use this as an alternative to actual API which will make them independent of backend team


|
App Engine Python SDK
v1.6.9 rev.445
The Python runtime is available as an experimental Preview feature.
|
Public Member Functions | |
| def | __new__ |
| def | __init__ |
| def | key |
| def | put |
| def | delete |
| def | is_saved |
| def | has_key |
| def | dynamic_properties |
| def | instance_properties |
| def | parent |
| def | parent_key |
| def | to_xml |
| def | get |
| def | get_by_key_name |
| def | get_by_id |
| def | get_or_insert |
| def | all |
| def | gql |
| def | from_entity |
| def | kind |
| def | entity_type |
| def | properties |
| def | fields |
Static Public Attributes | |
| save = put | |
Model is the superclass of all object entities in the datastore. The programming model is to declare Python subclasses of the Model class, declaring datastore properties as class members of that class. So if you want to publish a story with title, body, and created date, you would do it like this: class Story(db.Model): title = db.StringProperty() body = db.TextProperty() created = db.DateTimeProperty(auto_now_add=True) A model instance can have a single parent. Model instances without any parent are root entities. It is possible to efficiently query for instances by their shared parent. All descendents of a single root instance also behave as a transaction group. This means that when you work one member of the group within a transaction all descendents of that root join the transaction. All operations within a transaction on this group are ACID.
| def google.appengine.ext.db.Model.__init__ | ( | self, | |
parent = None , |
|||
key_name = None , |
|||
_app = None , |
|||
_from_entity = False , |
|||
| kwds | |||
| ) |
Creates a new instance of this model. To create a new entity, you instantiate a model and then call put(), which saves the entity to the datastore: person = Person() person.name = 'Bret' person.put() You can initialize properties in the model in the constructor with keyword arguments: person = Person(name='Bret') We initialize all other properties to the default value (as defined by the properties in the model definition) if they are not provided in the constructor. Args: parent: Parent instance for this instance or None, indicating a top- level instance. key_name: Name for new model instance. _from_entity: Intentionally undocumented. kwds: Keyword arguments mapping to properties of model. Also: key: Key instance for this instance, if provided makes parent and key_name redundant (they do not need to be set but if they are they must match the key).
| def google.appengine.ext.db.Model.__new__ | ( | args, | |
| unused_kwds | |||
| ) |
Allow subclasses to call __new__() with arguments. Do NOT list 'cls' as the first argument, or in the case when the 'unused_kwds' dictionary contains the key 'cls', the function will complain about multiple argument values for 'cls'. Raises: TypeError if there are no positional arguments.
| def google.appengine.ext.db.Model.all | ( | cls, | |
| kwds | |||
| ) |
Returns a query over all instances of this model from the datastore. Returns: Query that will retrieve all instances from entity collection.
| def google.appengine.ext.db.Model.delete | ( | self, | |
| kwargs | |||
| ) |
Deletes this entity from the datastore. Args: config: datastore_rpc.Configuration to use for this request. Raises: TransactionFailedError if the data could not be committed.
| def google.appengine.ext.db.Model.dynamic_properties | ( | self | ) |
Returns a list of all dynamic properties defined for instance.
| def google.appengine.ext.db.Model.entity_type | ( | cls | ) |
Soon to be removed alias for kind.
| def google.appengine.ext.db.Model.fields | ( | cls | ) |
Soon to be removed alias for properties.
| def google.appengine.ext.db.Model.from_entity | ( | cls, | |
| entity | |||
| ) |
Converts the entity representation of this model to an instance. Converts datastore.Entity instance to an instance of cls. Args: entity: Entity loaded directly from datastore. Raises: KindError when cls is incorrect model for entity.
| def google.appengine.ext.db.Model.get | ( | cls, | |
| keys, | |||
| kwargs | |||
| ) |
Fetch instance from the datastore of a specific Model type using key. We support Key objects and string keys (we convert them to Key objects automatically). Useful for ensuring that specific instance types are retrieved from the datastore. It also helps that the source code clearly indicates what kind of object is being retreived. Example: story = Story.get(story_key) Args: keys: Key within datastore entity collection to find; or string key; or list of Keys or string keys. config: datastore_rpc.Configuration to use for this request. Returns: If a single key was given: a Model instance associated with key for the provided class if it exists in the datastore, otherwise None. If a list of keys was given: a list where list[i] is the Model instance for keys[i], or None if no instance exists. Raises: KindError if any of the retreived objects are not instances of the type associated with call to 'get'.
| def google.appengine.ext.db.Model.get_by_id | ( | cls, | |
| ids, | |||
parent = None , |
|||
| kwargs | |||
| ) |
Get instance of Model class by id. Args: key_names: A single id or a list of ids. parent: Parent of instances to get. Can be a model or key. config: datastore_rpc.Configuration to use for this request.
| def google.appengine.ext.db.Model.get_by_key_name | ( | cls, | |
| key_names, | |||
parent = None , |
|||
| kwargs | |||
| ) |
Get instance of Model class by its key's name. Args: key_names: A single key-name or a list of key-names. parent: Parent of instances to get. Can be a model or key. config: datastore_rpc.Configuration to use for this request.
| def google.appengine.ext.db.Model.get_or_insert | ( | cls, | |
| key_name, | |||
| kwds | |||
| ) |
Transactionally retrieve or create an instance of Model class. This acts much like the Python dictionary setdefault() method, where we first try to retrieve a Model instance with the given key name and parent. If it's not present, then we create a new instance (using the *kwds supplied) and insert that with the supplied key name. Subsequent calls to this method with the same key_name and parent will always yield the same entity (though not the same actual object instance), regardless of the *kwds supplied. If the specified entity has somehow been deleted separately, then the next call will create a new entity and return it. If the 'parent' keyword argument is supplied, it must be a Model instance. It will be used as the parent of the new instance of this Model class if one is created. This method is especially useful for having just one unique entity for a specific identifier. Insertion/retrieval is done transactionally, which guarantees uniqueness. Example usage: class WikiTopic(db.Model): creation_date = db.DatetimeProperty(auto_now_add=True) body = db.TextProperty(required=True) # The first time through we'll create the new topic. wiki_word = 'CommonIdioms' topic = WikiTopic.get_or_insert(wiki_word, body='This topic is totally new!') assert topic.key().name() == 'CommonIdioms' assert topic.body == 'This topic is totally new!' # The second time through will just retrieve the entity. overwrite_topic = WikiTopic.get_or_insert(wiki_word, body='A totally different message!') assert topic.key().name() == 'CommonIdioms' assert topic.body == 'This topic is totally new!' Args: key_name: Key name to retrieve or create. **kwds: Keyword arguments to pass to the constructor of the model class if an instance for the specified key name does not already exist. If an instance with the supplied key_name and parent already exists, the rest of these arguments will be discarded. Returns: Existing instance of Model class with the specified key_name and parent or a new one that has just been created. Raises: TransactionFailedError if the specified Model instance could not be retrieved or created transactionally (due to high contention, etc).
| def google.appengine.ext.db.Model.gql | ( | cls, | |
| query_string, | |||
| args, | |||
| kwds | |||
| ) |
Returns a query using GQL query string. See appengine/ext/gql for more information about GQL. Args: query_string: properly formatted GQL query string with the 'SELECT * FROM <entity>' part omitted *args: rest of the positional arguments used to bind numeric references in the query. **kwds: dictionary-based arguments (for named parameters).
| def google.appengine.ext.db.Model.has_key | ( | self | ) |
Determine if this model instance has a complete key. When not using a fully self-assigned Key, ids are not assigned until the data is saved to the Datastore, but instances with a key name always have a full key. Returns: True if the object has been persisted to the datastore or has a key or has a key_name, otherwise False.
| def google.appengine.ext.db.Model.instance_properties | ( | self | ) |
Alias for dyanmic_properties.
| def google.appengine.ext.db.Model.is_saved | ( | self | ) |
Determine if entity is persisted in the datastore. New instances of Model do not start out saved in the data. Objects which are saved to or loaded from the Datastore will have a True saved state. Returns: True if object has been persisted to the datastore, otherwise False.
| def google.appengine.ext.db.Model.key | ( | self | ) |
Unique key for this entity. This property is only available if this entity is already stored in the datastore or if it has a full key, so it is available if this entity was fetched returned from a query, or after put() is called the first time for new entities, or if a complete key was given when constructed. Returns: Datastore key of persisted entity. Raises: NotSavedError when entity is not persistent.
| def google.appengine.ext.db.Model.kind | ( | cls | ) |
Returns the datastore kind we use for this model. We just use the name of the model for now, ignoring potential collisions.
| def google.appengine.ext.db.Model.parent | ( | self | ) |
Get the parent of the model instance. Returns: Parent of contained entity or parent provided in constructor, None if instance has no parent.
| def google.appengine.ext.db.Model.parent_key | ( | self | ) |
Get the parent's key. This method is useful for avoiding a potential fetch from the datastore but still get information about the instances parent. Returns: Parent key of entity, None if there is no parent.
| def google.appengine.ext.db.Model.properties | ( | cls | ) |
Returns a dictionary of all the properties defined for this model.
| def google.appengine.ext.db.Model.put | ( | self, | |
| kwargs | |||
| ) |
Writes this model instance to the datastore. If this instance is new, we add an entity to the datastore. Otherwise, we update this instance, and the key will remain the same. Args: config: datastore_rpc.Configuration to use for this request. Returns: The key of the instance (either the existing key or a new key). Raises: TransactionFailedError if the data could not be committed.
| def google.appengine.ext.db.Model.to_xml | ( | self, | |
_entity_class = datastore.Entity |
|||
| ) |
Generate an XML representation of this model instance. atom and gd:namespace properties are converted to XML according to their respective schemas. For more information, see: http://www.atomenabled.org/developers/syndication/ http://code.google.com/apis/gdata/common-elements.html


?6
7
8
9
10
TheNewYork
copyright
reserved
Botanical
Garden
NEW
YORK
BOTANIC^
GARDEN
"01264092
Flora
of
the
Bahamian
Archipelago
Family
Sapotaceae
Genus
Bumelia
Specific
epithet
salicifolia
Island
Abaco
Settlement
or
location
Abaco
National
Park
(L.O
Sw.
BOTANICAL
GARDE*4.
X
Coordinate
2881763
Habitat
Pine
woodland
Y
Coordinate
18R
027150
<0>IMAGED
Collector
1
Ethan
Freid
Other
Collectors
Brandi
Mueller
Collection
Date
Monday,
June
07,
2004
Identified
bv
Ethan
Freid
Collection
#
04-028
01264092
Legend -
Level of confidence that token is an accurately-transcribed word
extremely low
very low
low
undetermined
medium
high
very high


It is easily added to a web project, just like the other libraries and framework, and for that there are two ways.
To use it locally, we need to download the file and import it
Or, the minified one
Or, we can use...
Using CDN is a little bit simpler because the only thing that must be done is to import it
Or, we can use the minified one...


SQL Mustache
This work was inspired by the excellent Mustache templating system by Chris Wanstrath and other contributors which in turn was inspired by ctemplate).
This particular project is built to work with SQL Server 2016 or higher.
This is a "logic-less" simple templating system that allows templates to be stored in whatever manner makes sense.
It can be used for pretty much anything that demands a template.
This implementation is opinionated about formatting because it is important to maintain strict formatting with templates for message and even dynamic code generation.
Because of this, the templates produced will need to be sensitive to how the tags are layed out within the template.
The renderer assumes that if you put whitespace in the template that is was deliberate and every effort is made to respect all formatting.
Why You Might Use a Mustache Template
Use Mustache when you want to craft a data driven readable string that keeps formatting intact without a complex set of logic.
In many cases you are limited only by your imagination. Here are some examples.
Email Messaging system
Perhaps your application needs to produce email messages, you could fairly easily craft a templating system that you could even expose to end user authors.
Those authors could then use any variables translated to keys (or tags) within an email message template.
Machine Messaging system
It's often necessary to construct messages for queueing system that take many shapes.
In SQL this typically takes the shape of queries and string concatenation for XML, JSON, or some other proprietary message structure.
What looks better? This:
DECLARE @m NVARCHAR(max) = N'<message>
<id>' + FORMAT(@id, 'G') + N'</id>
<user>' + COALESCE(NULLIF(@user, ''), N'unknown') + N'</user>
<dto>' + COALESCE(@dto, GETUTCDATE()) + N'</dto>
</message>'
Or this:
DECLARE @m NVARCHAR(max) = N'<message>
<id>{{id}}</id>
<user>{{user}}</user>
<dto>{{dto}}</dto>
</message>'
Even Dynamic Code can be Templated Out
Whether you are a proponent of dynamics or not regardless of the technology that is being used, it's often necessary to engage in some kind of evaluated string.
I don't know about you, but I like to be able to at least read the insanity and that includes the formatting of the text within the template.
DECLARE @sqlText NVARCHAR(max) = N'
SELECT s.id {{fieldList}}
FROM #_tbl_stuff s
{{#where}}{{criteria}}{{/where}}
';
Maybe you think that's ugly, but it's far prettier than what often ends up as the alternative.
Usage - Hello World
PRINT mustache.s_render(
N'Hello {{locale}}'
, N'{"locale":"World"}'
);
Outputs:
Hello World
Here's the Demo example (as formatted):
DECLARE @template NVARCHAR(max) = N'
{{header}}

{{#bug}}
{{/bug}}
{{#items}}
{{#first}}

{{name}}

{{/first}}
{{#link}}

{{name}}

{{/link}}
{{/items}}
{{#empty}}

The list is empty.

{{/empty}}';
DECLARE @json NVARCHAR(max) = N'{
"header": "Colors",
"items": [
{"name": "red", "first": true, "url": "#Red"},
{"name": "green", "link": true, "url": "#Green"},
{"name": "blue", "link": true, "url": "#Blue"}
],
"empty": false
}';
PRINT mustache.s_render(@template, @json);
Which will output:

Colors

red

green

blue

Notice the liberal white space. The demo itself removes all white space and formatting.
The SQL Mustache implementation will respect all white space and formatting.
While it is not necessary to worry much about formatting when dealing with HTML, it is very necessary when dealing with other use cases such as a plain text email template, or a code template.
To get the appropriately formatted text (with a comment and unset variable added in), try this example:
DECLARE @template NVARCHAR(max) = N'
{{header}}

{{#bug}}
{{/bug}}

{{#items}} {{#first}}
{{name}}
{{/first}}{{#link}}
{{name}}
{{/link}}
{{/items}}

{{#empty}}
The list is empty.
{{/empty}}

{{notset}}{{! comment}}

';
DECLARE @json NVARCHAR(max) = N'{
"header": "Colors",
"items": [
{"name": "red", "first": true, "url": "#Red"},
{"name": "green", "link": true, "url": "#Green"},
{"name": "blue", "link": true, "url": "#Blue"}
],
"empty": false
}';
PRINT mustache.s_render(@template, @json);
Compare this output with that above:

Colors

red

green

blue

The list is empty.

Supported Functionality
Remember: this implementation does NOT assume you are working with HTML and formatting for your template is strictly respected!
[✔] Variables (Tags)
Template: A big animal = {{animal}}
Hash: { "animal" : "Elephant" }
Output: A big Animal = Elephant
[✔] Unescaped Variables (Tags)
Template:
this is {{{html}}} this is encoded {{html}}

Hash: {"html":"bold"}
Output:
this is bold this is encoded bold

[✔] Comments
Template: |{{! this is a comment }}|
Hash: {}
Output: ||
[✔] Sections
Template: {{#showMe}}Shown{{/showMe}}{{#hideMe}}Hidden{{/hideMe}}
Hash: { "showme" : true, "hideMe" : false }
Output: Shown
[✔] Nested Sections
Template: {{#outer}}{{#inner}}{{tag}}{{/inner}}{{/outer}}
Hash: { "outer": [{"inner": {"tag": 1}}, {"inner": {"tag": 2}}, {"inner": {"tag": 3}} ]}
Output: 123
[✔] Conditional Sections
Template: {{#people}} {{person}} {{/people}} {{^people}} No people {{/people}}
Hash: { "people": [] }
Output: No people
[✔] Conditional Sections - false
Template: {{#people}} {{person}} {{/people}} {{^people}} No people {{/people}}
Hash: { "people": false }
Output: No people
[❌] Partials
[❌] Lambdas
[❌] Set Delimiter
[❌] Error on Missing Tag in Object


Web Development
I help people build real and lasting relationships in a digital world. Services include custom websites to awe-inspiring experience that drive results.
I'm a creative web develper, photographer and an accomplished program manager based in Minneapolis, specializing in User Interface Design and Development. I build clean, appealing, and functional interfaces which comply with the latest web standards.
I'm curious by nature and a life long learner who loves developing new skills. I am passionate about adventure motorcycling, sky diving, web development, potrait photography and street food.
Being a multipotentialite is my destiny. Multipotentialites have no “one true calling” the way specialists do. I have many paths and I pursue all of them, either sequentially or simultaneously (or both). I am a multipotentialite.
I help people build real and lasting relationships in a digital world. Services include custom websites to awe-inspiring experience that drive results.
Photography is the universal language that speaks to the heart. A photos can move people worldwide without the need for a shared spoken language
I love to bring ideas to life. I beleive in asking the right questions to get the right answers. Lets stop talking about the future and lets start building it !!


grewalsatinder
02-Sep-18 11:52 AM
I don't know what others think or realise, but I understood from James' comment that just privacy alone is the strongest feature of PIRATE.
and that doesn't need a roadmap. it's a done feature. use it now feature.
bugs are found and thanks to the community we have this as a live chain
and that helps even better


Be it FIRST DATE, a COMPANY event, visiting night CLUB. We will always keep you confident.
Learn more


Six speeding tickets before his 18th birthday quashed Bill’s dreams of becoming a race-car driver, but his need for speed never diminished. At 14 he became the youngest Boy Scout in America, at the time, to receive...
See More
Six speeding tickets before his 18th birthday quashed Bill’s dreams of becoming a race-car driver, but his need for speed never diminished. At 14 he became the youngest Boy Scout in America, at the time, to receive the coveted Eagle Scout Award.
These days Bill takes the Boy Scout motto, “Be prepared”, seriously in his role of PMO Director. His preparedness allows him to respond to our clients in a ninja-quick fashion and still leaves him time to peruse Reddit, his non-guilty, guilty pleasure.
See Less


Fallout 4 is possibly the most hyped game to ever be released. Made by Bethesda Studios, this is the 7th game in the Fallout series. Players venture through a large world similar to skyrim, and complete various missions. The game also holds a huge amount of customizability options when it comes to your character and their guns.
The new Battlefront game is another game people have long awaited. The game positions players on a large battlefield with many other players, and you try to complete objectives. The game also comes out right around the same time as the new Star Wars movie, making everybody extremely excited for everything new in the Star Wars Universe.


Before Change
assert isinstance(training_batch, rlt.PreprocessedMemoryNetworkInput)
// mdnrnn"s input should have seq_len as the first dimension
mdnrnn_input = rlt.PreprocessedStateAction.from_tensors(
training_batch.state.float_features, training_batch.action
)
mdnrnn_output = self.memory_network(mdnrnn_input)
// mus, sigmas: [seq_len, batch_size, num_gaussian, state_dim]
mus, sigmas, logpi, rs, nts = (
mdnrnn_output.mus,
After Change
// mdnrnn"s input should have seq_len as the first dimension
mdnrnn_output = self.memory_network(
training_batch.state, rlt.FeatureData(training_batch.action)
)
// mus, sigmas: [seq_len, batch_size, num_gaussian, state_dim]
mus, sigmas, logpi, rs, nts = (
mdnrnn_output.mus,


This is my portfolio. I am currently learning how to code @DSC Unilag. I have always had an interest in technology and the impact it has on our daily lives. This is both from a software and hardware point of view. I previously built a couple drag and drop websites which never really fit my requirements. I am interested in transitioning into a more digital/technology focused role.
At this point of time I have a reasonable understanding of HTML, CSS, Bootstrap and some knowledge of jQuery and currently learning more about JavaScript. I can teach people the basics of code via Scratch, HTML, CSS and the occasional sprinkle of Javascript.


My setting this experience through would be the comforting intimacy of tripping alone in the early hours of the morning, in my room. I would be on a completely new substance, on perhaps a decidedly high dosage for being inexperienced with this chemical. This trip, I decided to avoid electronica in my play list of music(a mainstay of music for my psychedelically accelerated mind) and decided to otherwise choose vocally inclined artists including Modest Mouse(an older album), Jason Mraz, Dido, Rasputina, and Frank Sinatra.
I have been lucky enough to have been dealt(so to speak) a rather nice hand of drugs in my few short years of tripping. I've come to experience Salvia, Dxm, Amanitas, 2c-i, Lsd, 2c-t-2, Lsa, Mushrooms, Nitrous, Pfpp and a few others. I am 20 years old, weigh around 160lb, and am physically fit, although I wouldn't go so far as to say I'm 'built'. I exhibit a borderline personality, but am an otherwise stable individual. I take no medications, and supplement daily my diet with vitamins, an omega 3, 6, and 9 complex, and 100mg 5-htp, additionally I use kava nightly as a sleep aid. I pursue psychedelics for the wide range of experiences offered, and I rather like how these substances interact with my drawing capability. At the beginning of this experience I've eaten a number of cookies and a large, fast food restaurant's burger, finding myself edging towards being full.
02:05 – Imbibement
I insufflate the chemical, and lick the residual dust off the mirror, finding to my chagrin a powerfully unpleasant assault on my taste buds. The bitter, chemicalish taste doesn't linger and was easily dealt with by strong mints, though it would be recurring due to the drip. The nose load from this is mildly unpleasant, but I've only partook in snorting one other drug, yopo, and this happens to be a walk in the park comparatively speaking. However, the drip is rather unpleasant, and in the beginning I wondered if I would regret my method of ingestion, but found myself to have forgotten about it some ten minutes later.
02:15 – First Alert
My pupils have dilated, and while colors are a bit brighter, overt mental and visual effects are absent.
02:30 –
I've come up listening to music and find myself by this point able to pick out every instrument at my leisure. Visuals are still absent. I find it rather odd, I can feel my mind slipping into a significantly off baseline area where I'd usually expect visuals to follow, I'm excited to have found a chemical that packs a wallop for the mental experience while laying off trivial(in my opinion anyway) visuals.
At this point I decided to drop music from my activities and pursue drawing for a bit.
0300 – +++
I realize now that this is amongst the most pleasant body highs I've experienced, no jaw clenching, odd muscle tensions, balance issues, disorientation, or anything I can otherwise tell as negative. Instead, a slow warmth has begun creeping across my entire body, accompanied by a very mild mood push, while the tactile experience has seemingly been enhanced some fair bit. The mental experience is coming along very nicely, seemingly lacking those tightly spun loops I associate with 2c-i or mushrooms, and instead allowing for an extremely level headed perspective, without polarization any one way or the other(emotionally or reasonably speaking), with the added bonus of a creative push(or step away from my normally lethargic nature). I find I'm rather enjoying myself at this point.
04:00 –
I've recently called a friend, and found speech to be completely uninhibited, I'm a bit faster on the uptake of topics and such than normal, although rather tangent orientated, or a bit more easily distracted, but all of such is to have been expected in the heightened associative mindset of this kind of substance. I've decided to put my headphones back on and am greeted with something or other that is pleasant, from here on I proceed to continue drawing and find myself very relaxed. I find I am able to focus on drawing and the music separately and well, neither in the least bit distracting from the other. The euphoric warmth is quite comforting, as if like basking in the sun's glow, the tactile has not gotten much better or worse and by this point can't be expected to change much.
05:00 – On Comparisons
Over the past two hours I have peaked nicely, and found visuals akin to pfpp, that is with a general kick up of the visual scatter, brighter colors, a bit of a halo effect around stark contrasts, and mild breathing effects(most prominent in shadows and textured surfaces). The mental effects were significantly greater than pfpp and more akin to 2c-t-2, with perhaps a bit less overall euphoria. The body load seems to have been most comparable to 2c-i with regard to general temperature seemingly having been a bit warmer, slightly higher heart rate, and a greater appreciation for the tactile sense. From this point I proceed to ride the experience out to it's close, allowing myself to become distracted by the internet and little else. Typing well, at this point, does require a bit of effort and I seem to have a few more spelling errors than is usual. Otherwise, as with engaging in conversation, communicating on IRC and sending messages is not particularly difficult.
06:20 – Review
My pupils are back to baseline size, as am I back to normal with a pleasant afterglow. I am not at all fatigued by this experience, and do not feel any negative effects such as a slowdown, headache, or other undesirable. I did not find this substance to be overly stimulating or sedating, there was absolutely no nausea(though that's something I wouldn't normally experience except perhaps for by yopo), gastro effects were nothing special, and I very much so enjoyed myself. The change in scenery regarding my preference for music did nothing more than keep me for the most part uninterested and unengaged in the music, finding myself treating it more as distraction worthy than to be the beautiful sound scape I would under normal circumstances enjoy.
I rather think integrating the bit of enhancement to artistic capability from this will be rather easy, due to few gaps in my memory that I would otherwise experience on the vast majority of psychedelics I've done(perhaps simply an issue of length of time and relative impact of a tighter, faster looping thought process I seem to associate with tripping on most other substances), and the fact that the enhancement to my drawing ability was not in any sense 'mindblowing' but would be enough to warrant review and kick start the integration process from this aspect of my trip.
Written in the afterglow of the experience.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


MENU - ACTIVITIES
COMPLETED: {{ completedExercises }}/{{ exercises.length }}
Reset
{{ exercisePercentage }}% completed ({{ exerciseStats.ok }} of {{ exerciseStats.total }} tests).


To make using the API easier, we have built the Symbol Cache technology, which accepts strings and tries to guess which Symbol
object you might mean. With the Symbol Cache, many methods can accept a string such as "IBM" instead of a complete Symbol
object. We highly recommend you don't rely on this technology and instead save explicit references to your securities when you need them.
# Example 1: Relying On Symbol Cache: self.AddEquity("IBM") # Add by IBM string ticker, save reference to Symbol Cache. self.MarketOrder("IBM", 100) # Determine refering to IBM Equity from Symbol Cache. self.History("AAPL", 14) # Makes a guess referring to AAPL Equity. # Example 2: Correctly Using Symbols: self.ibm = self.AddEquity("IBM").Symbol # Add IBM Equity string ticker, save Symbol. self.MarketOrder(self.ibm, 100) # Use IBM Symbol in future API calls. self.aapl = Symbol.Create("AAPL", SecurityType.Equity, Market.USA) self.History(self.aapl, 14)
// Example 1: Relying On Symbol Cache: AddEquity("IBM"); // Add by IBM string ticker, save reference to Symbol Cache. MarketOrder("IBM", 100); // Determine refering to IBM Equity from Symbol Cache. History("AAPL", 14); // Guess referring to AAPL Equity. // Example 2: Correctly Using Symbols: var ibm = AddEquity("IBM").Symbol; // Add IBM Equity string ticker, save Symbol. MarketOrder(ibm, 100); // Use IBM Symbol in future API calls. var aapl = Symbol.Create("AAPL", SecurityType.Equity, Market.USA); History(aapl, 14);


// SPDX-License-Identifier: MIT
pragma solidity 0.7.4;
import "./TellorStorage.sol";
import "./TellorVariables.sol";
/**
@author Tellor Inc.
@title TellorMaster
@dev This is the Master contract which delegate calls to the Tellor contract.
* The logic implemented by this contract is saved on the TellorGetters, TellorStake,
* TellorTransfer, and Extension contracts
*/
contract TellorMaster is TellorStorage, TellorVariables {
event NewTellorAddress(address _newTellor);
constructor(address _tContract, address _oTellor) {
addresses[_OWNER] = msg.sender;
addresses[_DEITY] = msg.sender;
addresses[_TELLOR_CONTRACT] = _tContract;
addresses[_OLD_TELLOR] = _oTellor;
bytesVars[_CURRENT_CHALLENGE] = bytes32("1");
uints[_DIFFICULTY] = 100;
uints[_TIME_TARGET] = 240;
uints[_TARGET_MINERS] = 200;
uints[_CURRENT_REWARD] = 1e18;
uints[_DISPUTE_FEE] = 500e18;
uints[_STAKE_AMOUNT] = 500e18;
uints[_TIME_OF_LAST_NEW_VALUE] = block.timestamp - 240;
currentMiners[0].value = 1;
currentMiners[1].value = 2;
currentMiners[2].value = 3;
currentMiners[3].value = 4;
currentMiners[4].value = 5;
// Bootstraping Request Queue
for (uint256 index = 1; index < 51; index++) {
Request storage req = requestDetails[index];
req.apiUintVars[_REQUEST_Q_POSITION] = index;
requestIdByRequestQIndex[index] = index;
}
assembly {
sstore(_EIP_SLOT, _tContract)
}
emit NewTellorAddress(_tContract);
}
/**
* @dev This function allows the Deity to set a new deity
* @param _newDeity the new Deity in the contract
*/
function changeDeity(address _newDeity) external {
require(msg.sender == addresses[_DEITY]);
addresses[_DEITY] = _newDeity;
}
/**
* @dev This function allows the owner to set a new _owner
* @param _newOwner the new Owner in the contract
*/
function changeOwner(address _newOwner) external {
require(msg.sender == addresses[_OWNER]);
addresses[_OWNER] = _newOwner;
}
/**
* @dev allows for the deity to make fast upgrades. Deity should be 0 address if decentralized
* @param _tContract the address of the new Tellor Contract
*/
function changeTellorContract(address _tContract) external {
require(msg.sender == addresses[_DEITY]);
addresses[_TELLOR_CONTRACT] = _tContract;
assembly {
sstore(_EIP_SLOT, _tContract)
}
}
/**
* @dev This is the internal function that allows for delegate calls to the Tellor logic
* contract address
*/
function _delegate(address implementation) internal virtual {
// solhint-disable-next-line no-inline-assembly
assembly {
// Copy msg.data. We take full control of memory in this inline assembly
// block because it will not return to Solidity code. We overwrite the
// Solidity scratch pad at memory position 0.
calldatacopy(0, 0, calldatasize())
// Call the implementation.
// out and outsize are 0 because we don't know the size yet.
let result := delegatecall(
gas(),
implementation,
0,
calldatasize(),
0,
0
)
// Copy the returned data.
returndatacopy(0, 0, returndatasize())
switch result
// delegatecall returns 0 on error.
case 0 {
revert(0, returndatasize())
}
default {
return(0, returndatasize())
}
}
}
/**
* @dev This is the fallback function that allows contracts to call the tellor
* contract at the address stored
*/
fallback() external payable {
address addr = addresses[_TELLOR_CONTRACT];
_delegate(addr);
}
}
|


- {% if user.is_authenticated %} {% else %} {% endif %}
Ladakh is one of India's most serene locations. Its landscape is filled with the beauty and mystery of oriental philosophies and cultures. This region has a simple outlook towards life, living away from the pollution and commotion of modern India;


Prediction
The Team
Predict a Kickstarter Success
A project created by Mark Porath, Fadil Shaikh, Youssef Al-Yakoob, and Zack Rock
PREDICT YOUR SUCCESS


You can use Quick Look, in all the main views of MacHg: The Files View, The History View, and The Differences View. The shortcut to display the quicklook preview of the selected items is the standard OSX shortcut of the space bar.
Important:If you want to copy or select text from the QuickLook preview panel then you can double click on the previewed file to be taken to the snapshot of that file. The historical snapshots of the various files are stored in your Caches folder and are erased when you quit MacHg.
If not already in the Files View, switch to it (by selecting Files View from the Document View menu).
Select the files in the Files View you want to preview.
Open the QuickLook panel by any of:
Choose the menu item File > Open Quick Look Panel.
Choose the QuickLook toolbar item if you have included it in the tool bar.
Use the space bar shortcut on your keyboard.
If not already in the History View, switch to it (by selecting History View from the Document View menu).
Select the revisions in the History View for which you want to view snapshots of the files as of those revisions. If you select multiple revisions the preview of the files which will be shown will be the version of the file as of the most recent revision selected.
Open the QuickLook panel by any of:
Choose the menu item File > Open Quick Look Panel.
Choose the QuickLook toolbar item if you have included it in the tool bar.
Use the space bar shortcut on your keyboard.
If not already in the Differences View, switch to it (by selecting Differences View from the Document View menu).
Select the base revision and the compare revision in the Differences view.
The changes between the revisions are represented in virtual files listed in the file browser part of the view. Amongst these files choose the files you want to preview. The preview will show the state of the files at the time of the compare revision.
Open the QuickLook panel by any of:
Choose the menu item File > Open Quick Look Panel.
Choose the QuickLook toolbar item if you have included it in the tool bar.
Use the space bar shortcut on your keyboard.


The Full Monty
(British, 1997)
Seen on 10 September 1997 for $8/75 at the SONY 19th Street
In British slang, "the full monty" means to be completely naked. While *The Full Monty* is about a sextet of out-of-work blokes who decide to put on a strip-tease act, a lot more than their bodies are laid bare. "Sheffield, a city on the move!" cries a propaganda film of the 1950s, declaring a boom for Sheffield and all of Britain. This is a stark contrast to the present, where steel plants that once helped produced the flatware on your dining table lay idle and rusting. The streets are not bustling; Sheffield often looks like a ghost town. The ghosts in this case are the flesh-and-blood men who are out of work, on the dole, with time on their hands. The few jobs that are available are low-paying security guard j obs.
Robert Carlyle plays Gaz, a former steel worker who cannot afford to do much with his son Nathan (William Snape). Along with his friend Dave (Mark Addy), they meander around town like a pair of middle-aged teenagers.
A brief bit of eavesdropping in the mens room of a strip club catering to ladies for the evening gives Gaz an idea--he should form a strip troupe and raise money far quicker than the few jobs available to him. Then he would have money for child support, a nd joint custody.
What ensues is not unlike *The Seven Samurai*, with a highly entertaining and comic story of how the other members of the troupe are found and incorporated. First there is the suicidal Lomper (Steve Huison), followed by the middle manager cum dance instru ctor Gerald (Tom Wilkinson), pentagenarian Horse, who knows how to move, and Guy, who wants to dance like Gene Kelly but winds up flat on his back everytime he tries to walk up a wall. Luckily, Guy is extremely well endowed, which guarantees his entry to the troupe.
As is often the case with movies set in Great Britain, there is a lot of social commentary going on that often passes us by. Gerald lives a middle-class lifestyle (gnomes all over the garden) and has a house full on unnecessary luxuries.  Dave has sexual troubles with his wife, likely stemming from not having meaningful work. Many of these men had once been part of a great nation, and empire, and now, they haunt their old places and the high street has been supplanted by superstores and megamalls.
Men often define themselves by their work and their relative success in work defines their overall "worthiness." This is clear in The Full Monty. What makes it a winning movie is that the actors are able to pull off the dual roles of physical comedy and e motional depth. That is certainly no easy task, but the actors all pull it off. It's hard to find a favorite performance because they all do so well.
Some scenes to watch for: Dave and Gaz meet the Lomper; Gerald's interview; the six men waiting in line for the dole and spontaneously jiggling to the strains of Donna Summer's "Hot Stuff." The soundtrack is a lot of fun, too.
As with many British movies, you often wish subtitles were included. Luckily, the Full Monty website (via Fox Searchlight pictures) has a little phrasebook included.
---------------------------------- Copyright (c) 1997, Seth J. Bookey, New York, NY 10021<EMAIL_ADDRESS>Other movie reviews with graphics can be found at http://www.geocities.com/Athens/2679/kino.html.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Testing Picnic CSS
Check out the Flat example and the Official page
Button types
LinkColored buttons
Inputs
Input validation
Radio buttons
Note: this is optional, it will only appear if the <input>
is within a .radio
Checkbox
Note: this is optional, it will only appear if the <input>
is within a .checkbox
Select
Note: this is optional, it will only appear if the <select>
is within a .select
Modal
Tables
| Name | City | Age |
|---|---|---|
| Francisco | Valencia | 23 |
| Eve | San Francisco | 22 |
| Name | City | Age |
|---|---|---|
| Francisco | Valencia | 23 |
| Eve | San Francisco | 22 |
| Name | City | Age |
|---|---|---|
| Francisco | Valencia | 23 |
| Eve | San Francisco | 22 |
| Name | City | Age |
|---|---|---|
| Francisco | Valencia | 23 |
| Eve | San Francisco | 22 |
| Name | City | Age |
|---|---|---|
| Francisco | Valencia | 23 |
| Eve | San Francisco | 22 |
The second heading looks good enough
The third one also!
Does it provide a fourth one?
Even a fifth one!
This is a paragraph with some italics, both of them and a simple link plus another without href. Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
A simple list:
- First item
- Second item
An ordered list:
- First item
- Second item
Now we're going to quote someone
A person who never made a mistake never tried anything new.
Some inline
code goes here. Then some block code:
<link rel="stylesheet" type="text/css" href="//picnicss.com/dist/v2.css">


This example demonstrates blinking an LED with an Oscillator. See the Analog Output section of 'Using the Pin Object' guide and the Oscillator reference for more info.
Use the buttons below to start and stop blinking the LED.


# -*- coding: utf-8 -*-
from __future__ import absolute_import, division, print_function, unicode_literals
import sys
IGNORE_TRACEBACK = not ('--nosmalltb' in sys.argv or '--noignoretb' in sys.argv) # FIXME: dupliated in util_decor
# Module for funcs that need python 2 syntax to work in python 2
[docs]def ignores_exc_tb(*args, **kwargs):
"""
PYTHON 2 ONLY VERSION -- needs to be in its own file for syntactic reasons
ignore_exc_tb decorates a function and remove both itself
and the function from any exception traceback that occurs.
This is useful to decorate other trivial decorators
which are polluting your stacktrace.
if IGNORE_TRACEBACK is False then this decorator does nothing
(and it should do nothing in production code!)
References:
https://github.com/jcrocholl/pep8/issues/34 # NOQA
http://legacy.python.org/dev/peps/pep-3109/
"""
outer_wrapper = kwargs.get('outer_wrapper', True)
def ignores_exc_tb_closure(func):
if not IGNORE_TRACEBACK:
# if the global enforces that we should not ignore anytracebacks
# then just return the original function without any modifcation
return func
from utool import util_decor
#@wraps(func)
def wrp_noexectb(*args, **kwargs):
try:
return func(*args, **kwargs)
except Exception:
# Define function to reraise with python 2 syntax
#exc_type, exc_value, exc_traceback = sys.exc_info()
# Code to remove this decorator from traceback
# Remove two levels to remove this one as well
exc_type, exc_value, exc_traceback = sys.exc_info()
try:
exc_traceback = exc_traceback.tb_next
exc_traceback = exc_traceback.tb_next
#exc_traceback = exc_traceback.tb_next
except Exception:
print('too many reraise')
pass
raise exc_type, exc_value, exc_traceback
if outer_wrapper:
wrp_noexectb = util_decor.preserve_sig(wrp_noexectb, func)
return wrp_noexectb
if len(args) == 1:
# called with one arg means its a function call
func = args[0]
return ignores_exc_tb_closure(func)
else:
# called with no args means kwargs as specified
return ignores_exc_tb_closure


|
AbstractQueueWithResources
|
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BoundedAfterSubscriber
|
|
BoundedBeforeSubscriber
|
|
BoundedSubscriber
|
|
DirectedReference
|
|
EOFRuntimeException
|
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FileAccessor
|
|
FileBasedSPSCQueue
|
|
FileBasedSPSCQueueMemoryMapped
|
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FileBasedSPSCQueueMemoryMappedReaderWriter
|
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ForwarderOnSubscribe
|
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MapLastSubscriber
|
|
MappedByteBufferInputStream
|
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MappedByteBufferOutputStream
|
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MergeProducer
|
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Metric
|
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Mutable
|
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NotificationSubscriber
|
|
NullSentinel
|
|
ObservableServerSocket
|
|
OnSubscribeCacheResetable
|
|
OnSubscribeDoOnEmpty
|
|
OnSubscribeFromQueue
|
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OnSubscribeInputStream
|
|
OnSubscribeMapLast
|
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OnSubscribeReader
|
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OnSubscribeRepeating
|
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OnTerminateResumeSubscriber
|
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OperatorAddToSubscription
|
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OperatorBufferPredicateBoundary
|
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OperatorBufferToFile
|
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OperatorDoOnNth
|
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OperatorFromTransformer
|
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OperatorPassThroughAdjustedRequest
|
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OperatorSampleFirst
|
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OperatorUnsubscribeEagerly
|
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OperatorWindowMinMax
|
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OrderedMerge
|
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ParentSubscriber
|
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Permutations
|
|
PermutationsSwapIterator
|
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QueueProducer
|
|
QueueReader
|
|
QueueWithResources
|
|
QueueWithResourcesNonBlockingUnsubscribe
|
|
QueueWithSubscription
|
|
QueueWriter
|
|
RepeatingProducer
|
|
Result
|
|
ResumeSubscriber
|
|
RollingSPSCQueue
|
|
Singleton
|
|
SourceSubscriber
|
|
Swap
|
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TransformerDecode
|
|
TransformerDelayFinalUnsubscribe
|
|
TransformerLimitSubscribers
|
|
TransformerOnBackpressureBufferRequestLimiting
|
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TransformerOnTerminateResume
|
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TransformerStateMachine
|
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TransformerStringSplit
|
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UnsubscribedNotificationHolder
|


The light has daylight characters and can be adjusted per the operation
requirements.
Lights arms are made of Stainless Steel Fitting. The range of the color
temp
- 4,200 K Â±300 as natural color tone.
A cubicle curtain or hospital curtain is a dividing cloth used in a medical
treatment facility that provides a private enclosure for one or more
patients. curtain is usually made from inherently flame retardant (IFR)
fabric, and is suspended from a supporting structure or ceiling track.
All seven functions operated by high torque German make DC Motors.
C-arm compatible electrically controlled operation table with off-center
positioning of table top.
Equipped with high precision self lubricating German lifting column for
trouble
free, jerk less movements even at extreme off-center positions.
5 Standard parameters: ECG,RESP,NIBP,SPO2,1-TEMP.
Real time S-T segment analysis and ARR analysis.
Multi-display selectable including standards,large font,trend OxyCRG
dynamic.
Multi 7-lead ECG waveforms display in phase and capture dyanmic
waveform.
7 segment LEDS for systolic pressure,diastolic pressure SPO2 and pulse rate
2.4" color LCD for waveforms,bargraphic,trend and system menu
Weight:1.15 kg Dimension:232mm*198*75mm
3 vein infusion modes:Easy Speed-Setting Mode,Time-Volume Mode.
The system automatically opens up KVO.
The machine automaticallyrecords the settings for last injection.
Function to auto -Define a syring : complete calibration technology for
syring brand defination that ensures the compatibility of any syringe.
Simultaneous acquisition of 12-lead ECG data , real time recording
10.2"Inch LCD color display
Built-in analysis software of age which assures accurate analysis.
Paper size:210mm*20m
Data Transmitting to PC Via RS232/USB
8.4" color TFTLCD large Screen
The world's advanced DSP technology Double event marks
12 Hours Data & waveform Storage and review
Twins monitoring Function Option.
Power LED's emitting blue light in 450-470HM.
Long life>45,000 hrs
Intensity: >25/cm2
M.S. Fabrication with powder coated paint.
Operation Voltage:100-250 V AC,50Hz.
Large TFT screen to display the test result clearly.
Measure FVC,PEF,FEV1,etc.parameters synchronously.
communication with computer with USB, which can calculate data , analyze
trend and export.
Mainfold per point 10 gauge without Non Returen Valve and Copper Tail
Pipeline Connection.
Flexible tail pipe connection(Oxygen/Nitrous-Oxide/Carbon dioxide)-Superme
Model
Specially Designed Metal Knob Poly Carbonate Bottle Specially Made spindle
for
fine adjustment Adjustable redactor.
Comes with Gauge and Flowmeter Attached Humidifier Bottle Used with
oxygen
cylinder Precision functioning based Fine Adjustment Valves.
Our presented series of FA Valve and Flowmeter is enormously demanded in the
market for their long-lasting nature and superior finish.
- Longer life
- Excellent finish
Schradder Type Gas Outlet a perfect platform for handling various gases
including
oxygen and oxide.
The terminal unit/probe connection is of non-swivel type,so that second
equipment,such as a flow meter, is not tilted by the probe house.
Analog Gas Alaram System comes in 1-6 gas categories.
System contains analog meter for displaying pressure of gas from pipe
lines.
Alaram comes with RS-485 Modbus interface for BMS connectivity. One NO/NC
terminal is build in for alarm extension.
The main feature of Outlets Accept, retain and release the probe by means of
a
quick release mechanism designed for single - handed
operation. Outlets
contains Non-interchangeable specific gas / vacuum probes.
Area Valve Service Unit (AVSU) – provides local isolation of individual parts
of medical gas and vacuum piping system required for installation,
maintenance, or in the event of an emergency. AVSUs are installed in
each operating room, ICUs, resuscitation rooms and in other hospital areas.
A vacuum regulator controls pressure with inlet and outlet ports that let
higher pressure air into or out of a system as needed. The regulator's
outgoing airflow has a lower pressure than the air being drawn into the
vacuum pump.
Provision for two oxygen, one N20, two vacuum and one compressor air medical
gas outlets.
Available with pressure and vacuum gauges.
Twin 5/15 Amp electrical sockets.
Four splash proof type and one I.V hooks and two heavy duty stainless steel
monitor trays.
Load bearing capacity: Up to 70 kgs.
Movable arm length: 1000 mm.
Capitalizing on the ability of our dexterous and eminent crewmen, we are
indulged in the realm of providing Semi Automatic Oxygen Gas Control
Panel.
- Specifically designed
- Long lasting
ETL Listed to UL 1331 and CSA Z9170-1.
Conforms to NFPA 99 and CGA standards.
Accepts Puritan gas specific compatible male adaptors.
Gas specific indexing.
Removable front assembly.
All outlets are 100% tested for flow and leaks.
Nameplate color coding per NFPA 99.
360° swivel inlet tube.
British Standard Type gas Outlet are precisely designed to perfection to
provide
excellent performance. Outlets completely leack proof, safe and easy to
operate. Permanently identifiable with color for specific gas /vacuum
with
the name of the gas engraved on its face.
Non-interchangeable self-sealing quick connect gas outlet forOxg en/N 20/Va
cc u m/C.A.4/C.A.7/C02. Available for head wall unit/wall/pendent
units. Double locking (Secondary) facility. fascia of gas outlet
are interchangeable and gas colour coded.
Head-foot,height adjustmentfunction and +/- trendelenburg function can be
controlled by hand remote control.
Head part manually CPR function and also has electric CPR function.
Bed platform has four plastic lying surface
Height Adjustable.
Size 750mm*450mm*800mm
Framework made of s.s. tubes
Mounted on 100mm swivelling castors
Two stainless steel shelves with three side railings on both shelves
Foot and head boards are made of high quality ABS.
Aluminium collapsible type side rails.
5"luxiurious castor with brake .
IV pole provision on all four corners.
Seats with PU Cushion Perfect for Indoor use, Three perforated Seats on a
beam.
Cold rolled steel seat and back.
Cold rolled steel armrest and legs with chrome finishings.
Powder-coated cross beam in black.
The excellent selected Material
Environmental protection
Decresing eyes fatigue
The excellent flim clamping set
Timing Turn off
Automatic sensing function
Hospital Curtains polyester fabric water repellment
Hospital Curtain polyester fabric fire retardant,anti microbial and anti
fungal up to 50 washes
curtain size is 4*7 feet


---
layout: default
---
{% include trainingBanner.html %}
Path towards functional programming, reactive architecture and fast data
Training on Scala, Akka, Java8, Playframework, Lagom, hadoop, Spark
"We have number of years experience in scala and in it's ecosystem, we provide training on Lightbend technologies.
"checkout our training courses."
{% for member in site.data.courseInfo %} {% assign topics = member.curriculam.topics%} {% assign loopindex = forloop.index%}
{% endfor %}
{% for member in site.data.courseInfo %} {% assign topics = member.curriculam.topics%} {% assign loopindex = forloop.index%}
{% endfor %}


Lapland is the largest and northernmost region of Finland.
And it borders the Northern Ostrobothnia of Finland in the south, Gulf of Bothnia, Sweden, Norway
and Russia. It is as close as reality gets to those who dream of a winter wonderland. It is the home
of reindeer, Santa Claus and the Northern Lights.
To present this wonderland, I decide to create this sketch map in the cartoon style with Adobe
Sketch and Adobe Illustrator. Cartoon symbols depicts the most featured winter activities or
cultures
in different regions and their locations.


This is a 9"x12" watercolor on Strathmore 140lb cold press paper using Crayola Watercolor. Circa 2002.
The Process
This piece was fairly early in my artistic exploration and I was actually using Crayola watercolor for the large majority of the piece. I only had one brush, the brush that came with the Crayola set so this piece took quite a long time to finish, 15 hours approximately. Rather than going on thinly like you would normally expect for watercolor, the paint went on thickly to give the dark color. I would not likely ever sell the original given the nature of the media: Crayola puts a detergent along with excessive gum arabic in their watercolor which is great for keeping it from staining surfaces it makes it less than ideal for permanency thus a print is likely going to be much more permanent than the original.
The Meaning
This piece is fairly straight forward as it was inspired by an unrequited love interest. It was a very sad period for me because I thought I found a girl that was
The One
and yet
The One
didn't seem to want anything to do with me. This piece is symbolic of me figuring out that nothing was going to happen between the two of us. The upshot of all of this is that this piece was quite cathartic to finish and helped me come to terms with the truth.


Beaver plays a musical piece with several instruments.
Place the instruments in front of Beaver so that he can play them.
To get all the points, use only moves. Be careful about which instruments you replace!
In this version, you can grab in a single move, two instruments that are next to eachother. To do this, drag one of the instruments towards one of the instruments next to it, before dropping them in front of Beaver.


news
General updates of my study, research and publications.
| Feb 22, 2023 | new Published journal paper as a corresponding author titled "Structural causal model with expert augmented knowledge to estimate the effect of oxygen therapy on mortality in the icu" in Artificial Intelligence in Medicine 2023 (IF:7.011). |
|---|---|
| Sep 6, 2022 | new Peer reviewer for "AAAI 2023". |
| Jun 6, 2022 | Started as Informatics Postdoctoral Scholar at OCTRI, OHSU. |
| Apr 11, 2022 | Peer reviewer for "UAI 2022". |
| Apr 8, 2022 | Published conference paper as a co-author titled "mTOCS: Mobile Teleophthalmology in Community Settings to improve Eye-health in Diabetic Population" in COMPSAC 2022. |
| Mar 25, 2022 | Peer reviewer for "COMPSAC 2022". |
| Mar 4, 2022 | Successfully defended Ph.D. dissertation works, titled "Causal Inference in Healthcare: Approaches to Causal Modeling and Reasoning through Graphical Causal Models". |
| Feb 14, 2022 | Peer reviewer for "ACM CHIL 2022". |
| Nov 16, 2021 | Peer reviewer for "AISTATS 2022". |
| Nov 14, 2021 | Published journal paper as a first author titled "Design and development of myFAMI application framework: a mHealth self-management intervention for family members of pediatric transplant recipients" in JMIR Nursing, 2021. |
| Nov 9, 2021 | Presented poster on "Causal Fusion from Pragmatic Clinical Trial and Observational Study with Structural Causal Models" in A Celebration of Research Poster Session 2021 at Marquette University. |
| Jul 1, 2021 | Published journal paper as a co-author titled "Feasibility and Acceptability of a mHealth Self-Management Intervention for Pediatric Transplant Families" in Western Journal of Nursing Research, 2021. |
| May 28, 2021 | Published journal paper as a co-author titled "One Size Does Not Fit All: Discharge Teaching and Child Challenging Behaviors" in Western Journal of Nursing Research, 2021. |
| Apr 26, 2021 | Invited talk on "Introduction to Causal Inference" (slides) for COSC 5931/4931 Machine Learning Principles, Spring 2021 at Marquette University. |
| Apr 23, 2021 | Peer reviewer for "AMIA 2021". |
| Mar 26, 2021 | Member of Program Committee for "COMPSAC 2021". |
| Mar 19, 2021 | Awarded the Computer Science Summer Research Fellowship (CSSRF) for Summer 2021 at Marquette University. |
| Mar 3, 2021 | Peer reviewer for "Workshop on Machine Learning for Preventing and Combating Pandemics" in ICLR 2021. |
| Mar 1, 2021 | Member of Program Committee for "UAI 2021". |
| Dec 7, 2020 | Student volunteer for "NeurIPS 2020". |
| Nov 27, 2020 | Peer reviewer for "Workshop on Trustworthy AI for Healthcare" in AAAI 2021. |
| Nov 5, 2020 | Presented poster on "“Is Haloperidol effective in Delirium treatment?”: Answering Treatment and Policy Controversies in Healthcare through Prior Knowledge-driven Structural Causal Model Generation" in Forward Thinking Poster Session 2020 at Marquette University. |
| Aug 7, 2020 | Published conference paper titled "A Causally Formulated Hazard Ratio Estimation through Backdoor Adjustment on Structural Causal Model", presented in Machine Learning for Healthcare 2020. |
| May 29, 2020 | Passed Ph.D. qualifiying exam and completed Ph.D. proposal defense. |
| May 6, 2020 | Peer reviewer for "AMIA 2020". |
| May 5, 2020 | Published journal paper as a co-author titled "Using the engaging parents in education for discharge (ePED) iPad application to improve parent discharge experience" in Journal of Pediatric Nursing, Volume 52, 2020. |
| May 1, 2020 | Completed Master of Science in Computing (Specialization: Big Data and Data Analysis) from Deprtment of Computer Science, Marquette University. |
| Feb 27, 2020 | Published journal paper as a co-author titled "Pilot study protocol of a mHealth self‐management intervention for family members of pediatric transplant recipients" in Research in Nursing & Health, Volume 43, Issue 2, 2020. |
| Dec 3, 2019 | Presented poster on "Causal Exploration on Large Healthcare Datasets through Emulation of Randomized Controlled Trials: An Application on Delirium Patients in the ICU" in Forward Thinking Poster Session 2019 at Marquette University. |
| Nov 4, 2019 | Worked as co-author in a poster titled "MARQUECare: A Model Palliative Care System for Everyone" in A Celebration of Research in the Klingler College of Arts & Sciences 2019 at Marquette University. |
| Sep 27, 2019 | Worked as co-author in a poster titled "MARQUECare: Information Technology Tools and a System for Palliative Care for Everyone", presented in Healthy People on a Healthy Planet Conference, Milwaukee, WI. |
| Sep 26, 2019 | Worked as co-author in a poster titled "mTEH: A Decision Support System for Tele-ophthalmology to Improve Eye Health of Wisconsin Population in Community Settings", presented in the 4th IEEE/ACM Conference on Connected Health (CHASE), Washington, D.C. |
| Aug 19, 2019 | Shifted from Department of Mathematics, Statistics and Computer Science to Department of Computer Science. Started Ph.D. in Computer Science, changing from Ph.D. in Computational Sciences. |
| Aug 16, 2019 | Worked as co-author in a poster on "mHealth family self-management intervention for parents of transplanted children: Preliminary results", presented in 14th International Family Nursing Association Annual Meeting, Washington, D.C. |
| Jul 15, 2019 | Published conference paper as a corresponding author titled "Towards Predicting Risky Behavior among Veterans with PTSD by Analyzing Gesture Patterns", presented in IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC 2019), Milwaukee, WI. |
| Jul 15, 2019 | Published conference paper as a corresponding author titled "Analyzing Happiness: Investigation on Happy Moments using a Bag-of-words Approach and Related Ethical Discussions", presented in IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC 2019), Milwaukee, WI. |
| May 22, 2019 | Worked as co-author in a poster on "Outcomes of Use of Antipsychotic for Delirium in the ICU: A Big Data Approach", presented in American Thoracic Society 2019 International Conference, Dallas, TX. |
| May 20, 2019 | Started working as Visiting Scholar (Summer Research Intern) at Regenstrief Center for Healthcare Engineering, Purdue University, IN. |
| May 19, 2019 | Worked as co-author in a poster on "E-BAP: A scalable and flexible web-based software system to support self-management for behavior changes", presented in IEEE International Conference on Biomedical and Health Informatics (BHI'19), Chicago, IL. |
| May 2, 2019 | Attended the workshop "IRSA Conference: Causal Inference and Data Science" at University of Minnesota, MN. |
| Apr 25, 2019 | Attended the conference "AAAI 2019 Spring Symposium Series", specifically the WHY19 symposium "Beyond Curve Fitting — Causation, Counterfactuals and Imagination-Based AI" at Stanford University, Palo Alto, CA. |
| Mar 15, 2019 | Worked as Co-author in a poster on "mHealth family self-management intervention for parents of transplanted children: Preliminary results", presented in NIH K Scholars Workshop, Washington, D.C. |
| Nov 3, 2018 | Published conference paper as a co-author titled "Smarthelp: Smartphone-based hemoglobin level prediction using an artificial neural network" in AMIA 2018. |
| Oct 5, 2018 | Attended workshop on "Machine Learning 4 Medical Imaging (ML4MI)" at University of Wisconsin-Madison. |
| Sep 13, 2018 | Worked as Co-author in a poster on "Getting Started: mHealth family self-management intervention for parents of transplanted children", presented in Studies of Pediatric Liver Transplantation Annual Meeting. Atlanta, GA. |
| Jul 1, 2018 | Published conference paper as a co-author titled "A Culturally Tailored Intervention System for Cancer Survivors to Motivate Physical Activity" in COMPSAC 2018. |
| Apr 26, 2018 | Presented poster on "Survey Management and Image Grading Software System for Automated Detection of Diabetic Retinopathy to Improve Eye Health in the Milwaukee Community" in Community Engagement Conference at Medical College of Wisconsin, Milwaukee. |
| Nov 27, 2017 | Presented poster on "Non-invasive Human Identification using Wi-fi Signals" in Forward Thinking Poster Session 2017 at Marquette University. |


BritishNationalGrid¶
-
class
cm4twc.
BritishNationalGrid
(projection_y_coordinate, projection_x_coordinate, projection_y_coordinate_bounds, projection_x_coordinate_bounds, altitude=None, altitude_bounds=None)¶ Bases:
cm4twc.space.Grid
BritishNationalGrid characterises the spatial dimension for a
Component
as a regular grid on a cartesian domain whose coordinates are northings and eastings covering Great Britain and Northern Ireland (EPSG:27700).Instantiation
- Parameters
- projection_y_coordinate: one-dimensional array-like object
The array of northing coordinates in metres defining a spatial dimension of the domain. May be any type that can be cast to a
numpy.ndarray
. Must contain numerical values. Coordinates must be ordered positively.Parameter example:
projection_y_coordinate=[12500, 13500, 14500]
Parameter example:
projection_y_coordinate=numpy.arange(12500, 15500, 1000)
- projection_x_coordinate: one-dimensional array-like object
The array of easting coordinates in metres defining a spatial dimension of the domain. May be any type that can be cast to a
numpy.ndarray
. Must contain numerical values. Coordinates must be ordered positively.Parameter example:
projection_x_coordinate=(80500, 81500, 82500, 83500)
Parameter example:
projection_x_coordinate=numpy.arange(80500, 84500, 1000)
- projection_y_coordinate_bounds: two-dimensional array-like object
The array of northing coordinate bounds in metres defining the extent of the grid cell around the coordinate. May be any type that can be cast to a
numpy.ndarray
. Must be two dimensional with the first dimension equal to the size of projection_y_coordinate and the second dimension equal to 2. Must contain numerical values.Parameter example:
projection_y_coordinate_bounds=[ [12e3, 13e3], [13e3, 14e3], [14e3, 15e3] ]
Parameter example:
projection_y_coordinate_bounds=numpy.column_stack( (numpy.arange(12000, 15000, 1000), numpy.arange(13000, 16000, 1000)) )
- projection_x_coordinate_bounds: two-dimensional array-like object
The array of easting coordinate bounds in metres defining the extent of the grid cell around the coordinate. May be any type that can be cast to a
numpy.ndarray
. Must be two dimensional with the first dimension equal to the size of projection_x_coordinate and the second dimension equal to 2. Must contain numerical values.Parameter example:
projection_x_coordinate_bounds=((80e3, 81e3), (81e3, 82e3), (82e3, 83e3), (83e3, 84e3))
Parameter example:
projection_x_coordinate_bounds=numpy.column_stack( (numpy.arange(80000, 84000, 1000), numpy.arange(81000, 85000, 1000)) )
- altitude: one-dimensional array-like object, optional
The array of altitude coordinates in metres defining a spatial dimension of the domain (with upwards as the positive direction). May be any type that can be cast to a
numpy.ndarray
. Must contain numerical values. Ignored if altitude_bounds not also provided.Parameter example:
altitude=[10]
- altitude_bounds: two-dimensional array-like object, optional
The array of altitude coordinate bounds in metres defining the extent of the grid cell around the coordinate (with upwards as the positive direction). May be any type that can be cast to a
numpy.ndarray
. Must be two dimensional with the first dimension equal to the size of altitude and the second dimension equal to 2. Must contain numerical values. Ignored if altitude not also provided.Parameter example:
altitude_bounds=[[0, 20]]
Examples
>>> import numpy >>> sd = BritishNationalGrid( ... projection_y_coordinate=[12500, 13500, 14500], ... projection_x_coordinate=(80500, 81500, 82500, 83500), ... projection_y_coordinate_bounds=numpy.column_stack( ... (numpy.arange(12000, 15000, 1000), ... numpy.arange(13000, 16000, 1000)) ... ), ... projection_x_coordinate_bounds=numpy.column_stack( ... (numpy.arange(80000, 84000, 1000), ... numpy.arange(81000, 85000, 1000)) ... ), ... altitude=[10], ... altitude_bounds=[[0, 20]] ... ) >>> print(sd) BritishNationalGrid( shape {Z, Y, X}: (1, 3, 4) Z, altitude (1,): [10] m Y, projection_y_coordinate (3,): [12500, 13500, 14500] m X, projection_x_coordinate (4,): [80500, ..., 83500] m Z_bounds (1, 2): [[0, 20]] m Y_bounds (3, 2): [[12000, ..., 15000]] m X_bounds (4, 2): [[80000, ..., 84000]] m )
Methods¶
Construction
Instantiate a |
|
Instantiate a |
Comparison
Compare equality between the BritishNationalGrid and the spatial (X, Y, and Z) dimension coordinate in a |
|
Compare equality in region spanned between the RotatedLatLonGrid and another instance of RotatedLatLonGrid. |
Utility
Perform the movement of the given variable values from their current location to the next nearest receiving neighbour according to the flow_direction property of the Grid. |
Attributes¶
Return the size of the SpaceDomain dimension axes as a |
|
Return the name of the properties to use to get access to the axes defined for the SpaceDomain instance as a tuple. |
|
Return the X-axis of the SpaceDomain instance as a |
|
Return the Y-axis of the SpaceDomain instance as a |
|
Return the Z-axis of the SpaceDomain instance as a |
|
Return the bounds of the X-axis of the SpaceDomain instance as a |
|
Return the bounds of the Y-axis of the SpaceDomain instance as a |
|
Return the bounds of the Z-axis of the SpaceDomain instance as a |
|
Return the name of the X-axis of the SpaceDomain instance as a |
|
Return the name of the Y-axis of the SpaceDomain instance as a |
|
Return the name of the Z-axis of the SpaceDomain instance as a |
|
The information necessary to move any variable laterally (i.e. |


Karnataka Bypolls: BJP In Commanding Position As It Leads In 11 Out Of 15 Seats; Congress Admits Defeat
by Swarajya StaffThe B S Yediyurappa led Karnataka BJP has put itself in a commanding position in the Karnataka by-polls by leading in 11 out of the 15 assembly by-poll seats, News18 Kannada has reported.
The Congress which too contested all the 15 assembly seats is leading in two seats while the H D Kumaraswamy led JD(S) is leading on one.
Senior Congress leader D K Shivaumar was quoted as admitting defeat by claiming that the rebel MLAs have been accepted by the people.
This by-poll was necessitated after 15 Congress MLAs decided to rebel in the state which led to the dismissal of the Kumaraswamy led government. The rebels were later disqualified by the Supreme Court which led to the by-polls.
The BJP currently has 106 seats in the state and if the trends stand, it will comfortably surpass the majority mark of 113.


--- layout: post status: publish published: true title: No Trackbacks for you! author: display_name: Tomas Restrepo login: tomasr email<EMAIL_ADDRESS>url: http://winterdom.com/ author_login: tomasr author_email<EMAIL_ADDRESS>author_url: http://winterdom.com/ wordpress_id: 444 wordpress_url: http://winterdom.com/2006/10/notrackbacksforyou date: '2006-10-11 07:44:54 +0000' date_gmt: '2006-10-11 07:44:54 +0000' categories: - Blogging tags: [] comments: - id: 311 author: Tim Rayburn author_email<EMAIL_ADDRESS>author_url: http://www.timrayburn.net date: '2006-10-16 23:41:51 +0000' date_gmt: '2006-10-16 23:41:51 +0000' content: | You might want to check out my recent post at : http://www.timrayburn.net/2006/10/17/Upgrading+To+DasBlog+196288.aspx Which addresses this exact problem and offers a solution. ---
Like others, I had to finally cave in and disable trackbacks on my blog for now. I deleted more than 400 spam trackbacks over the weekend, or, rather, I had to delete the same 20 spam trackbacks on a single entry over and over again.
Maybe I'll implement a way in dasBlog to delete spam trackbacks/pingbacks in an easier and faster way, as deleting one by one is just way too painful with the current implementation.
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Provides an interface for the nVenn algorithm (Perez-Silva et al. 2018) <doi:10.1093/bioinformatics/bty109>. This algorithm works for any number of sets, and usually yields pleasing and informative Venn diagrams with proportionality information. However, representing more than six sets takes a long time and is hard to interpret, unless many of the regions are empty. If you cannot make sense of the result, you may want to consider 'UpSetR' <https://cran.r-project.org/package=UpSetR/README.html>.
| Version: | 0.2.1 |
| Depends: | R (≥ 2.10) |
| Imports: | Rcpp |
| LinkingTo: | Rcpp |
| Suggests: | knitr, rmarkdown, rsvg, grImport2 |
| Published: | 2018-05-11 |
| Author: | Victor Quesada [aut, cre, cph] |
| Maintainer: | Victor Quesada <quesadavictor at uniovi.es> |
| License: | MIT + file LICENSE |
| NeedsCompilation: | yes |
| Materials: | NEWS |
| CRAN checks: | nVennR results |
| Reference manual: | nVennR.pdf |
| Vignettes: |
Using nVennR to generate and explore n-dimensional, quasi-proportional Venn diagrams |
| Package source: | nVennR_0.2.1.tar.gz |
| Windows binaries: | r-devel: nVennR_0.2.1.zip, r-release: nVennR_0.2.1.zip, r-oldrel: nVennR_0.2.1.zip |
| OS X binaries: | r-release: nVennR_0.2.1.tgz, r-oldrel: nVennR_0.2.1.tgz |
| Old sources: | nVennR archive |
Please use the canonical form https://CRAN.R-project.org/package=nVennR to link to this page.